3D-Aware Video Generation

作者：Sherwin Bahmani, J. Park, Despoina Paschalidou, Hao Tang, Gordon Wetzstein, L. Guibas, L. Gool, R. Timofte · 发表于：Trans. Mach. Learn. Res. · 年份：2022 · DOI：10.48550/arxiv.2206.14797 · 被引用次数：24 · 研究领域：Computer Science

Generative models have emerged as an essential building block for many image synthesis and editing tasks. Recent advances in this field have also enabled high-quality 3D or video content to be generated that exhibits either multi-view or temporal consistency. With our work, we explore 4D generative adversarial networks (GANs) that learn unconditional generation of 3D-aware videos. By combining neural implicit representations with time-aware discriminator, we develop a GAN framework that synthesizes 3D video supervised only with monocular videos. We show that our method learns a rich embedding of decomposable 3D structures and motions that enables new visual effects of spatio-temporal renderings while producing imagery with quality comparable to that of existing 3D or video GANs.